Spatial panel-data models using Stata

Federico Belotti, Gordon Hughes () and Andrea Piano Mortari

Stata Journal, 2017, vol. 17, issue 1, 139-180

Abstract: xsmle is a new user-written command for spatial analysis. We consider the quasi–maximum likelihood estimation of a wide set of both fixed- and random-effects spatial models for balanced panel data. xsmle allows users to han- dle unbalanced panels using its full compatibility with the mi suite of commands, use spatial weight matrices in the form of both Stata matrices and spmat objects, compute direct, indirect, and total marginal effects and related standard errors for linear (in variables) specifications, and exploit a wide range of postestimation features, including the panel-data case predictors of Kelejian and Prucha (2007, Regional Science and Urban Economics 37: 363–374). Moreover, xsmle allows the use of margins to compute total marginal effects in the presence of nonlinear specifications obtained using factor variables. In this article, we describe the command and all of its functionalities using simulated and real data.

Keywords: xsmle; spatial analysis; spatial autocorrelation model; spatial autoregressive model; spatial Durbin model; spatial error model; generalized spa- tial panel random-effects model; panel data; maximum likelihood estimation (search for similar items in EconPapers)
Date: 2017
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Working Paper: Spatial panel data models using Stata (2016)
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